{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Logistic回归作业 利用Logistic回归技术实现糖尿病发病预测"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "一、\t数据说明： Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集） 根据现有的医疗信息预测5年内皮马印第安人糖尿病发作的概率。\n",
    "数据链接：https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes\n",
    "p.s.: Kaggle也有一个Practice Fusion Diabetes Classification任务，可以试试:)\n",
    "https://www.kaggle.com/c/pf2012-diabetes\n",
    "\n",
    "1)\t文件说明\n",
    "pima-indians-diabetes.csv：数据文件\n",
    "\n",
    "2)\t字段说明\n",
    "数据集共9个字段: \n",
    "pregnants：怀孕次数\n",
    "Plasma_glucose_concentration：口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度\n",
    "blood_pressure：舒张压，单位:mm Hg\n",
    "Triceps_skin_fold_thickness：三头肌皮褶厚度，单位：mm\n",
    "serum_insulin：餐后血清胰岛素，单位:mm\n",
    "BMI：体重指数（体重（公斤）/ 身高（米）^2）\n",
    "Diabetes_pedigree_function：糖尿病家系作用\n",
    "Age：年龄\n",
    "Target：标签， 0表示不发病，1表示发病\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "1.\t对数据做数据探索分析（可参考0_EDA_ diabetes.ipynb，不计分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#首先 import 必要的模块\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"C:\\\\XYX_use\\\\AI_course\\\\AI_learn\\\\AI_homework\\\\week5\\\\pima-indians-diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "从上图看出有些数据最小值是0，下面统计一下各个特征中0的数目，0的数目很多时，对应特征的直接采信意义不大，需要特殊处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names]==0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#该问题为分类问题，类别型特征直方图可用countplot,查看不同类的对应样本数目\n",
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "样本中0类型的样本相对1类型的样本较多，不过总体上两者相差还好，可以在回归分析时增加权重，也可以不加，简单起见后面可以使用balance形式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#各个特征自己在不同类型中的分布情况\n",
    "for feature in train.columns:\n",
    "    sns.violinplot(x='Target', y=feature, data=train, hue=\"Target\")\n",
    "    plt.xlabel('Diabetes', fontsize=12)\n",
    "    plt.ylabel(feature, fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27527ce4748>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#各个特征之间的相关性，并用热力图画出来\n",
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "上图看出，各个特征之间的相关性不是很大"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "2.\t适当的特征工程（可参考1_FE_ diabetes.ipynb，不计分）"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "对于特征值中大量0值情况（这里0进行处理，是因为在当前案例中，一些0值表示采样缺失），将其替换成null状态，方便后续进行处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0, np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "通过补中值的方式将各个特征里对应的缺失值补上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       0\n",
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "medians = train.median() \n",
    "train = train.fillna(medians)\n",
    "\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>121.656250</td>\n",
       "      <td>72.386719</td>\n",
       "      <td>29.108073</td>\n",
       "      <td>140.671875</td>\n",
       "      <td>32.455208</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>30.438286</td>\n",
       "      <td>12.096642</td>\n",
       "      <td>8.791221</td>\n",
       "      <td>86.383060</td>\n",
       "      <td>6.875177</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.750000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>121.500000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    121.656250       72.386719   \n",
       "std      3.369578                     30.438286       12.096642   \n",
       "min      0.000000                     44.000000       24.000000   \n",
       "25%      1.000000                     99.750000       64.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     29.108073     140.671875   32.455208   \n",
       "std                       8.791221      86.383060    6.875177   \n",
       "min                       7.000000      14.000000   18.200000   \n",
       "25%                      25.000000     121.500000   27.500000   \n",
       "50%                      29.000000     125.000000   32.300000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "对特征数据进行标准化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "#  get labels\n",
    "y_train = train['Target']   \n",
    "X_train = train.drop([\"Target\"], axis=1)\n",
    "\n",
    "#用于保存特征工程之后的结果\n",
    "feat_names = X_train.columns\n",
    "\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将特征工程后的数据存为csv格式\n",
    "X_train = pd.DataFrame(columns = feat_names, data = X_train)\n",
    "\n",
    "train = pd.concat([X_train, y_train], axis = 1)\n",
    "\n",
    "train.to_csv('C:\\\\XYX_use\\\\AI_course\\\\AI_learn\\\\AI_homework\\\\week5\\\\FE_pima-indians-diabetes.csv',index = False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.544261e-17</td>\n",
       "      <td>7.661695e-18</td>\n",
       "      <td>-1.123956e-17</td>\n",
       "      <td>-1.795800e-16</td>\n",
       "      <td>4.416317e-17</td>\n",
       "      <td>2.815312e-16</td>\n",
       "      <td>2.398978e-16</td>\n",
       "      <td>1.857600e-16</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.141852e+00</td>\n",
       "      <td>-2.552931e+00</td>\n",
       "      <td>-4.002619e+00</td>\n",
       "      <td>-2.516429e+00</td>\n",
       "      <td>-1.467353e+00</td>\n",
       "      <td>-2.074783e+00</td>\n",
       "      <td>-1.189553e+00</td>\n",
       "      <td>-1.041549e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.448851e-01</td>\n",
       "      <td>-7.201630e-01</td>\n",
       "      <td>-6.937615e-01</td>\n",
       "      <td>-4.675972e-01</td>\n",
       "      <td>-2.220849e-01</td>\n",
       "      <td>-7.212087e-01</td>\n",
       "      <td>-6.889685e-01</td>\n",
       "      <td>-7.862862e-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.509521e-01</td>\n",
       "      <td>-1.530732e-01</td>\n",
       "      <td>-3.198993e-02</td>\n",
       "      <td>-1.230129e-02</td>\n",
       "      <td>-1.815412e-01</td>\n",
       "      <td>-2.258989e-02</td>\n",
       "      <td>-3.001282e-01</td>\n",
       "      <td>-3.608474e-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.399473e-01</td>\n",
       "      <td>6.112653e-01</td>\n",
       "      <td>6.297816e-01</td>\n",
       "      <td>3.291706e-01</td>\n",
       "      <td>-1.554775e-01</td>\n",
       "      <td>6.032562e-01</td>\n",
       "      <td>4.662269e-01</td>\n",
       "      <td>6.602056e-01</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.906578e+00</td>\n",
       "      <td>2.542658e+00</td>\n",
       "      <td>4.104082e+00</td>\n",
       "      <td>7.955377e+00</td>\n",
       "      <td>8.170442e+00</td>\n",
       "      <td>5.042397e+00</td>\n",
       "      <td>5.883565e+00</td>\n",
       "      <td>4.063716e+00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  7.680000e+02                  7.680000e+02    7.680000e+02   \n",
       "mean   2.544261e-17                  7.661695e-18   -1.123956e-17   \n",
       "std    1.000652e+00                  1.000652e+00    1.000652e+00   \n",
       "min   -1.141852e+00                 -2.552931e+00   -4.002619e+00   \n",
       "25%   -8.448851e-01                 -7.201630e-01   -6.937615e-01   \n",
       "50%   -2.509521e-01                 -1.530732e-01   -3.198993e-02   \n",
       "75%    6.399473e-01                  6.112653e-01    6.297816e-01   \n",
       "max    3.906578e+00                  2.542658e+00    4.104082e+00   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin           BMI  \\\n",
       "count                 7.680000e+02   7.680000e+02  7.680000e+02   \n",
       "mean                 -1.795800e-16   4.416317e-17  2.815312e-16   \n",
       "std                   1.000652e+00   1.000652e+00  1.000652e+00   \n",
       "min                  -2.516429e+00  -1.467353e+00 -2.074783e+00   \n",
       "25%                  -4.675972e-01  -2.220849e-01 -7.212087e-01   \n",
       "50%                  -1.230129e-02  -1.815412e-01 -2.258989e-02   \n",
       "75%                   3.291706e-01  -1.554775e-01  6.032562e-01   \n",
       "max                   7.955377e+00   8.170442e+00  5.042397e+00   \n",
       "\n",
       "       Diabetes_pedigree_function           Age      Target  \n",
       "count                7.680000e+02  7.680000e+02  768.000000  \n",
       "mean                 2.398978e-16  1.857600e-16    0.348958  \n",
       "std                  1.000652e+00  1.000652e+00    0.476951  \n",
       "min                 -1.189553e+00 -1.041549e+00    0.000000  \n",
       "25%                 -6.889685e-01 -7.862862e-01    0.000000  \n",
       "50%                 -3.001282e-01 -3.608474e-01    0.000000  \n",
       "75%                  4.662269e-01  6.602056e-01    1.000000  \n",
       "max                  5.883565e+00  4.063716e+00    1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "3.\t采用5折交叉验证，分别用log似然损失和正确率，对Logistic回归模型的正则超参数调优。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "读取特征工程后的数据，然后进行5折交叉验证的分配"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:\\\\XYX_use\\\\AI_course\\\\AI_learn\\\\AI_homework\\\\week5\\\\FE_pima-indians-diabetes.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = df[\"Target\"]\n",
    "\n",
    "X = df.drop([\"Target\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(384, 8)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样50%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.5)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "似乎在使用LR回归时，不用人为先将数据分成测试样本和训练样本，在LogisticRegressionCV函数的cv参数就是交叉验证的折数？？？是不是这样？？？？？？？？？？？？？？？？？？？？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(384, 8)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存特征名字以备后用（可视化）\n",
    "feat_names = X_train.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对稀疏矩阵进行压缩\n",
    "from scipy.sparse import csr_matrix\n",
    "X_train = csr_matrix(X_train)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "log似然损失进行正则超参数调优"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "正则化的 Logistic Regression及参数调优\n",
    "\n",
    "logistic回归的需要调整超参数有：C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和正则函数penalty（L2/L1） 目标函数为：J = C* sum(logloss(f(xi), yi)) +* penalty \n",
    "在sklearn框架下，不同学习器的参数调整步骤相同： 设置参数搜索范围 生成GridSearchCV的实例（参数） 调用GridSearchCV的fit方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#需要调优的参数\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.48500223264775544\n",
      "{'C': 0.1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pd.DataFrame(grid.cv_results_).to_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#cvresult = pd.DataFrame.from_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#test_means = cv_results['mean_test_score']\n",
    "#test_stds = cv_results['std_test_score'] \n",
    "#train_means = cvresult['mean_train_score']\n",
    "#train_stds = cvresult['std_train_score'] \n",
    "\n",
    "\n",
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    #plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "上图给出了L1正则和L2正则下、不同正则参数C对应的模型在训练集上测试集上的logloss。可以看出在训练集上C越大（正则越少）的模型性能越好"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "正确率进行超参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='accuracy')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7864583333333334\n",
      "{'C': 10, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:125: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pd.DataFrame(grid.cv_results_).to_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#cvresult = pd.DataFrame.from_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#test_means = cv_results['mean_test_score']\n",
    "#test_stds = cv_results['std_test_score'] \n",
    "#train_means = cvresult['mean_train_score']\n",
    "#train_stds = cvresult['std_train_score'] \n",
    "\n",
    "\n",
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    #plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
